diff --git a/src/extraction/core/extractor.py b/src/extraction/core/extractor.py
index 6a5af6bcbfef415ede6d3c0b2ca70b276c86bcb0..25668e6a0bbd38aee9a6af8d01745352678aa3fe 100644
--- a/src/extraction/core/extractor.py
+++ b/src/extraction/core/extractor.py
@@ -39,7 +39,7 @@ RED_FUNS = load_redfuns()
class ExtractorParameters(ParametersABC):
"""
- Base class to define parameters for extraction
+ Base class to define parameters for extraction.
"""
def __init__(
@@ -55,7 +55,7 @@ class ExtractorParameters(ParametersABC):
Nested dictionary indicating channels, reduction functions and
metrics to be used.
str channel -> U(function,None) reduction -> str metric
- If not of depth three, tree will be filled with Nones.
+ If not of depth three, tree will be filled with None.
sub_bg: set
multichannel_ops: dict
"""
@@ -66,7 +66,7 @@ class ExtractorParameters(ParametersABC):
@staticmethod
def guess_from_meta(store_name: str, suffix="fast"):
"""
- Find the microscope used from the h5 metadata
+ Find the microscope used from the h5 metadata.
Parameters
----------
@@ -91,25 +91,28 @@ class ExtractorParameters(ParametersABC):
class Extractor(ProcessABC):
"""
- The Extractor applies a metric, such as area or median, to cells identified in the image tiles using the cell masks.
+ Apply a metric to cells identified in the tiles.
- Its methods therefore require both tile images and masks.
+ Using the cell masks, the Extractor applies a metric, such as area or median, to cells identified in the image tiles.
- Usually one metric is applied to the masked area in a tile, but there are metrics that depend on the whole tile.
+ Its methods require both tile images and masks.
- Extraction follows a three-level tree structure. Channels, such as GFP, are the root level; the second level is the reduction algorithm, such as maximum projection; the last level is the metric - the specific operation to apply to the cells in the image identified by the mask, such as median, which is the median value of the pixels in each cell.
+ Usually the metric is applied to only a tile's masked area, but some metrics depend on the whole tile.
+
+ Extraction follows a three-level tree structure. Channels, such as GFP, are the root level; the reduction algorithm, such as maximum projection, is the second level; the specific metric, or operation, to apply to the masks is the third level.
Parameters
----------
parameters: core.extractor Parameters
- Parameters that include with channels, reduction and
- extraction functions to use.
+ Parameters that include the channels, and reduction and
+ extraction functions.
store: str
- Path to hdf5 storage file. Must contain cell outlines.
+ Path to the h5 file, which must contain the cell masks.
tiler: pipeline-core.core.segmentation tiler
- Class that contains or fetches the image to be used for segmentation.
+ Class that contains or fetches the images used for segmentation.
"""
+ # Alan: should this data be stored here or all such data in a separate file
default_meta = {
"pixel_size": 0.236,
"z_size": 0.6,
@@ -150,7 +153,7 @@ class Extractor(ProcessABC):
store: str,
tiler: Tiler,
):
- # initate from tiler
+ """Initiate from a tiler instance."""
return cls(parameters, store=store, tiler=tiler)
@classmethod
@@ -160,12 +163,12 @@ class Extractor(ProcessABC):
store: str,
img_meta: tuple,
):
- # initiate from image
+ """Initiate from images."""
return cls(parameters, store=store, tiler=Tiler(*img_meta))
@property
def channels(self):
- # returns a tuple of strings of the available channels
+ """Get a tuple of the available channels."""
if not hasattr(self, "_channels"):
if type(self.params.tree) is dict:
self._channels = tuple(self.params.tree.keys())
@@ -226,7 +229,7 @@ class Extractor(ProcessABC):
self._all_funs = {**self._custom_funs, **FUNS}
def load_meta(self):
- # load metadata from h5 file whose name is given by self.local
+ """Load metadata from h5 file."""
self.meta = load_attributes(self.local)
def get_tiles(
@@ -237,8 +240,9 @@ class Extractor(ProcessABC):
**kwargs,
) -> t.Optional[np.ndarray]:
"""
- Finds traps for a given time point and given channels and z-stacks.
- Returns None if no traps are found.
+ Find tiles for a given time point and given channels and z-stacks.
+
+ Returns None if no tiles are found.
Any additional keyword arguments are passed to tiler.get_tiles_timepoint
@@ -378,7 +382,6 @@ class Extractor(ProcessABC):
self.reduce_dims(trap, method=RED_FUNS[red_fun])
for trap in traps
]
-
d = {
red_fun: self.extract_funs(
metrics=metrics,
@@ -395,6 +398,7 @@ class Extractor(ProcessABC):
) -> np.ndarray:
"""
Collapse a z-stack into 2d array using method.
+
If method is None, return the original data.
Parameters
@@ -419,7 +423,7 @@ class Extractor(ProcessABC):
**kwargs,
) -> t.Dict[str, t.Dict[str, t.Dict[str, tuple]]]:
"""
- Core extraction method for an individual time-point.
+ Extract for an individual time-point.
Parameters
----------
@@ -561,7 +565,7 @@ class Extractor(ProcessABC):
def get_imgs(self, channel: t.Optional[str], traps, channels=None):
"""
- Returns the image from a correct source, either raw or bgsub
+ Return image from a correct source, either raw or bgsub.
Parameters
----------
@@ -586,7 +590,7 @@ class Extractor(ProcessABC):
def run_tp(self, tp, **kwargs):
"""
- Wrapper to add compatiblibility with other steps of the pipeline.
+ Wrapper to add compatibility with other steps of the pipeline.
"""
return self.run(tps=[tp], **kwargs)
@@ -680,7 +684,7 @@ def flatten_nesteddict(
nest: dict, to="series", tp: int = None
) -> t.Dict[str, pd.Series]:
"""
- Converts a nested extraction dict into a dict of pd.Series
+ Convert a nested extraction dict into a dict of pd.Series.
Parameters
----------
@@ -709,6 +713,7 @@ def flatten_nesteddict(
class hollowExtractor(Extractor):
"""
Extractor that only cares about receiving images and masks.
+
Used for testing.
"""